Artificial Intelligence-Assisted Modelling of High-Rate Ductile Fracture

Supervisors:

Dr Emmanouil Kakouris, Engineering
Prof. James Kermode, Engineering

High-rate ductile fracture, particularly in scenarios such as shock loading, poses a significant challenge in engineering, as existing models often fail to represent the complex interplay of plastic deformation, strain localisation, and void formation.

This project seeks to enhance the phase-field method, enabling more accurate predictions of fracture under dynamic conditions. State-of-the-art computational techniques combined with insights from advanced physics will be employed to improve the robustness and applicability of fracture modelling.

Artificial intelligence will support this effort, accelerating parameter calibration and facilitating uncertainty quantification for greater accuracy and reliability.

Development of DFT methods to study atomic structure and pressure effects in f- electron materials

Supervisors:

Prof. Julie Staunton, Physics
Dr. Albert Bartok-Partay, Eng/Physics

Rare earth materials are in increasing demand making good modelling of their electrons important for further development. The elements have similar chemistry owing to common valence electronic structure but varying numbers of nearly bound f-electrons, which determine magnetic properties. Using recent advances in modelling alloys and f-electron effects in magnets, this project will develop Density Functional Theory methodology for multicomponent lanthanide materials. We will study, for example, how application of pressure causes the f-electrons in cerium-rich alloys to delocalise and join the valence electrons triggering a dramatic change in properties. The project will explore building machine learning interatomic potentials for further modelling.

Machine Learning-Driven Molecular Simulations of Gas Transport in Polymeric Materials

Supervisors:

Prof. Gabriele Sosso, Chemistry
Dr Lukasz Figiel, Warwick Manufacturing Group (WMG)
Prof. James Kermode, Engineering

This project utilises advancing machine learning techniques for simulating gas transport in polymeric materials.

Specifically, we will leverage the MACE machine learning interatomic potential framework to improve the current models of gas diffusion and solubility in polymers, thus addressing several industry-relevant challenges. In particular, we seek to understand how aging affects these materials.

The work involves molecular dynamics simulations, electronic structure calculations, and machine learning to develop accurate and efficient models.

This project will lead to a robust computational framework to predict material behaviour and degradation over time.

Unlocking the Mysteries of Metallic Phase Transitions

Supervisors:

Dr Livia Partay, Chemistry
Dr Albert Bartok, Eng/Physics

Join a PhD project that goes beyond state-of-the-art to explore the intriguing phase behaviour of potassium and unlock new understanding of alkali metals’ unique physical properties. At high pressures and temperatures, these metals reveal complex phase transitions that remain poorly understood with exotic structures emerging that are not seen in any other material. This project combines cutting-edge sampling techniques with machine-learned potentials for accurate phase predictions, offering considerable opportunity for method development with broad, long-term impact. Not only will you gain insights into fundamental atomistic properties of alkali metals, but you’ll also contribute to pioneering computational tools that extend far beyond potassium.